A lot of interesting economic models with uncertainty and heterogeneity cannot be solved analytically using pen and paper. They can only be solved numerically using a computer. This spring I am offering a new course for bachelor and master students on solving and simulating well-known micro and macro economic models from e.g. the 2nd year courses. Its called "Introduction to Programming and Numerical Analysis" (see also the preliminaryGitHub page).

The course requires no prior experience with programming.

The first part of the course introduces you to programming using the general-purpose Python language. You will learn to write conditional statements, loops, functions, and classes, and to print results and produce static and interactive plots. You will learn to solve simple numerical optimization problems, and draw random number and run simulations. You will learn to test, debug and document your code, and use online communities proactively when writing code.

The second part of the course give you a brief introduction on how to import data from offline and online sources, structure it, and produce central descriptive statistics. You will learn to estimate simple statistical models on your data.

The third part of the course introduce you to the concept of a numerical algorithm. You will learn how to write simple searching, sorting and optimization algorithms. You will learn to solve linear algebra problems, solve non-linear equations numerically and symbolically, find fixed points, and solve complicated numerical optimization problems relying on function approximation.

You will get hands-on experience with applying the above techniques to solve well-known microeconomic and macroeconomic problems from the core bachelor courses. Specifically, you will work with both a small data analysis project, and a larger model analysis project based on a well-known economic model.

While the course only focus on programming in Python, you will also be equipped to start learning other programming languages (such as MATLAB, R, Julia or even C/C++) on your own.

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